Field of the Invention
The present invention is directed in general to field of information processing. In one aspect, the present invention relates to a system and method for signal processing and control signaling for wireless MIMO communication systems.
Description of the Related Art
Wireless communication systems transmit and receive signals within a designated electromagnetic frequency spectrum, but the capacity of the electromagnetic frequency spectrum is limited. As the demand for wireless communication systems continues to expand, there are increasing challenges to improve spectrum usage efficiency. To improve the communication capacity of the systems while reducing the sensitivity of the systems to noise and interference and limiting the power of the transmissions, a number of wireless communication techniques have been proposed, such as Multiple Input Multiple Output (MIMO), which is a transmission method involving multiple transmit antennas and multiple receive antennas. For example, space division multiple access (SDMA) systems can be implemented as closed-loop systems to improve spectrum usage efficiency. SDMA has recently emerged as a popular technique for the next generation communication systems. SDMA based methods have been adopted in several current emerging standards such as IEEE 802.16 and the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) platform.
FIG. 1 depicts a wireless MIMO communication system 100 that employs SDMA. In MIMO systems, transmitters and receivers are both equipped with multiple antennas. The wireless communication system 100 includes one or more transmitters 101 (e.g., base stations) and one or more receiver stations 102.l-102.m (e.g., subscriber stations), where “m” is an integer representing the number of receiver stations in a given geographic area. Base stations and subscriber stations can be both transmitters and receivers when both base stations and subscriber stations are equipped with a receiver and a transmitter. Base stations generally communicate with multiple subscriber stations. Subscriber stations communicate directly with a base station and indirectly, via the base station, with other subscriber stations. The number of base stations depends in part on the geographic area to be served by the wireless communication system 100. Subscriber systems can be virtually any type of wireless one-way or two-way communication device such as a cellular telephones, wireless equipped computer systems, and wireless personal digital assistants. The signals communicated between base stations and subscriber stations can include voice, data, electronic mail, video, and other data, voice, and video signals.
In an SDMA-MIMO wireless communication system, each base station 101 and subscriber station 102.i includes an array of antennas for transmitting and receiving signals. In SDMA, different subscriber stations share the same time-frequency channel and the separation between them occurs in the spatial dimension. During transmission, the antenna array forms a beam or multiple beams by applying a set of transmit beam forming weights to signals applied to each antenna in the antenna array. A different set of transmit beam forming weights is applied by the base station to each communication with each subscriber station with a goal of minimizing interference between the radio communication devices signals. In some transmission schemes, such as time division duplex (TDD), beam forming between the base station and subscriber stations allows the allocation of the same frequency channel and different time channel to subscriber stations during downlink and uplink. In other transmission schemes, such as frequency division duplex (FDD), beam forming between the base station and subscriber stations allows the allocation of the same time channel and different frequency channel to subscriber stations during downlink and uplink.
As depicted more specifically in FIG. 1, the MIMO system base station 101 uses beamforming to transmit a single data stream (e.g., s1) through multiple antennas, and the receiver combines the received signal from the multiple receive antennas to reconstruct the transmitted data. This is accomplished with “beamforming” weights whereby a signal si is processed for transmission by applying a weight vector wi to the signal si and transmitting the result xi over an array of antennas. The weighting vector wi is used to direct the signal with the objective of enhancing the signal quality or performance metric, like signal-to-interference-and-noise ratio (SINR) of the received signal. At the receiver, the received signals detected by the array of antennas are processed using a combining vector vi. In particular, the base station 101 has an array of N antennas 105, where N is an integer greater than or equal to m. The base station prepares a transmission signal, represented by the vector xi, for each signal si, where i∈{1, 2, . . . , m}. (Note: lower case bold variables indicate vectors and upper case BOLD variables indicate matrices). The transmission signal vector xi is determined in accordance with Equation [1]:xi=wi·si  [1]where wi, is the ith beamforming, N dimensional transmission weight vector (also referred to as a “transmit beamformer”), and each coefficient wj of weight vector wi represents a weight and phase shift on the jth transmit antenna 105. In addition, the term “si” is the data to be transmitted to the ith receiver. Each of the coefficients of weight vector wi may be a complex weight. Unless otherwise indicated, transmission beamforming vectors are referred to as “weight vectors,” and reception vectors are referred to as “combining vectors,” though in systems having reciprocal channels (such as TDD systems), a combining vector v at a receiver/subscriber station can be used as both a combining vector (when receiving signals from a transmitter/base station) and a weighting vector (when transmitting to a transmitter/base station).
The transmission signal vector xi is transmitted via a channel represented by a channel matrix Hi. The channel matrix Hi represents a channel gain between the transmitter antenna array 105 and the receive antenna array 104.i at the ith subscriber station 102.i. Thus, the channel matrix Hi can be represented by a N×ki matrix of complex coefficients, where N is the number of antennas at the base station antenna array 105 and ki is the number of antennas in the ith subscriber station antenna array 104.i. The value of ki can be unique for each subscriber station. As will be appreciated, the channel matrix Hi can instead be represented by a ki×N matrix of complex coefficients, in which case the matrix manipulation algorithms are adjusted accordingly so that, for example, the right singular vector calculation on a N×ki channel matrix becomes a left singular vector calculation on a ki×N channel matrix. The coefficients of the channel matrix Hi depend, at least in part, on the transmission characteristics of the medium, such as air, through which a signal is transmitted. A variety of methods may be used to determine the channel matrix Hi coefficients, such as transmitting a known pilot signal to a receiver so that the receiver, knowing the pilot signal, can estimate the coefficients of the channel matrix Hi using well-known pilot estimation techniques. Alternatively, the actual channel matrix Hi is known to the receiver and may also be known to the transmitter.
At the subscriber station 102.i, the transmitted signals are received on the ki receive antennas. For example, the transmission signal vector xi is transmitted via a channel represented by a channel matrix H1, and is received at the receiver 102.l as a receive signal vector y1=H1Hx1+n1 (where n represents noise and any co-channel interference caused by other subscriber stations). More specifically, the received signals for the ith subscriber station 102.i are represented by a ki×1 received signal vector yi in accordance with Equation [2]:
                              y          i                =                                            s              i              *                        ⁢                          H              i              H                        ⁢                          w              i                                +                      (                                                            ∑                                      n                    =                    1                                    m                                ⁢                                                                  ⁢                                                      s                    n                    *                                    ⁢                                      H                    i                    H                                    ⁢                                      w                    n                                                              -                                                s                  i                  *                                ⁢                                  H                  i                  H                                ⁢                                  w                  i                                                      )                                              [        2        ]            where “si” is the data to be transmitted to the ith subscriber station 102.i, “sn” is the data transmitted to the nth subscriber station 102.n, the * superscript denotes the complex conjugation operator, “HiH” represents the complex conjugate transpose of the channel matrix correlating the base station 101 and ith subscriber station 102.i, wi is the ith transmit weight vector, and wn is the nth transmit weight vector. The superscript “H” is used herein as a hermitian operator to represent a complex conjugate transpose operator. The jth element of the received signal vector yi represents the signal received on the jth antenna of subscriber station 102.i, j∈{1, 2, . . . , ki}. The first term on the right hand side of Equation [2] is the desired receive signal while the summation terms less the desired receive signal represent co-channel interference. Finally, to obtain a data signal, zi, which is an estimate of the transmitted data the subscriber station 102.i combines the signals received on the k antennas using a combining vector vi in accordance with Equation [3]:zi=ŝi=yiHvi.  [3]
While the benefits of MIMO are realizable when the receiver 102 alone knows the communication channel, these benefits are further enhanced in “closed-loop” MIMO systems when the transmitter 101 has some level of knowledge concerning the channel response between each transmitter antenna element and each receive antenna element of a receiver 102.i. Precoding systems provide an example application of closed-loop systems which exploit channel-side information at the transmitter (“CSIT”). With precoding systems, CSIT can be used with a variety of communication techniques to operate on the transmit signal before transmitting from the transmit antenna array 105. For example, precoding techniques can be used at the base station 101 to provide a multi-mode beamformer function to optimally match the input signal on one side to the channel on the other side so that multiple users or subscriber stations can be simultaneously scheduled on the same time-frequency resource block (RB) by separating them in the spatial dimension. This is referred to as a space division multiple access (SDMA) system or as a multi-user (MU)-MIMO system. Additional examples of precoding include using a channel quality indicator (CQI) value measured at a receiver 102.i to perform adaptive modulation and coding (AMC) on the transmit signal before transmission to the receiver 102.i. 
While full broadband channel knowledge may be obtained at the transmitter 101 by using uplink sounding techniques (e.g., with Time Division Duplexing (TDD) systems), most precoded MIMO systems (e.g., with TDD or Frequency Division Duplexing (FDD) systems) use channel feedback techniques to measure channel information at the receiver 102.i and then feed back the measured channel information to the transmitter 101. However, it is difficult to accurately measure the channel information or associated channel characteristics (such as SINR or channel quality information (CQI)) for a particular receiver when the communication status of other receivers in the vicinity is not known. In an SDMA system, this results from the fact that signal information being sent to other receivers can appear as interference or noise at the intended receiver 102.i, though the receiver can not be expected to have this knowledge when the channel characteristics are being measured.
Another difficulty associated with channel feedback techniques is the large overhead required for providing full channel feedback. One way of addressing this difficulty is to quantize the channel information prior to feedback. Usually, quantization is done by selecting a precoding codeword from a preset codebook known to both the transmitter and receiver, and sending only an index corresponding the selected codeword, thereby reducing the amount of feedback as compared to the high overhead of full channel feedback. However, the quantization techniques used in existing codebook systems to compress the channel feedback information can introduce inaccuracies in the feedback signal, causing losses in the zero-forcing/interference reduction properties of MU-MIMO beamforming. Feedback quantization also causes inaccuracies when the base station uses an estimate of the channel quality indicator (like SINR) that is computed at the receiver, which leads to loss in performance. Moreover, the limited feedback resources require that any practical system be designed to have a low feedback rate, and existing codebook systems can have unacceptably high feedback data rates.
Accordingly, there is a need for an improved system and methodology for signal processing and control signaling in a MIMO-SDMA system. There is also a need for a multi-user MIMO system which accurately estimates channel quality indicator information at a particular receiver without requiring knowledge of the other receivers or the base station scheduling algorithm. In addition, there is a need for a family of signal processing algorithms for selecting transmit and receive array vectors for MIMO-SDMA which overcomes quantization-related precoding errors and other problems in the art, such as outlined above. Further limitations and disadvantages of conventional processes and technologies will become apparent to one of skill in the art after reviewing the remainder of the present application with reference to the drawings and detailed description which follow.